Nanoscale reference materials for environmental, health and safety measurements: needs, gaps and opportunities
Bibliographic record
Abstract
The authors critically reviewed published lists of nano-objects and their physico-chemical properties deemed important for risk assessment and discussed metrological challenges associated with the development of nanoscale reference materials (RMs). Five lists were identified that contained 25 (classes of) nano-objects; only four (gold, silicon dioxide, silver, titanium dioxide) appeared on all lists. Twenty-three properties were identified for characterisation; only (specific) surface area appeared on all lists. The key themes that emerged from this review were: 1) various groups have prioritised nano-objects for development as "candidate RMs" with limited consensus; 2) a lack of harmonised terminology hinders accurate description of many nano-object properties; 3) many properties identified for characterisation are ill-defined or qualitative and hence are not metrologically traceable; 4) standardised protocols are critically needed for characterisation of nano-objects as delivered in relevant media and as administered to toxicological models; 5) the measurement processes being used to characterise a nano-object must be understood because instruments may measure a given sample in a different way; 6) appropriate RMs should be used for both accurate instrument calibration and for more general testing purposes (e.g., protocol validation); 7) there is a need to clarify that where RMs are not available, if "(representative) test materials" that lack reference or certified values may be useful for toxicology testing and 8) there is a need for consensus building within the nanotechnology and environmental, health and safety communities to prioritise RM needs and better define the required properties and (physical or chemical) forms of the candidate materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".